Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations3647
Missing cells3692
Missing cells (%)11.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory549.4 B

Variable types

Numeric2
Text3
Categorical3
Unsupported1

Alerts

CUIT is highly overall correlated with estadoCivil and 3 other fieldsHigh correlation
estadoCivil is highly overall correlated with CUITHigh correlation
nacionalidad is highly overall correlated with CUITHigh correlation
nroDoc is highly overall correlated with CUITHigh correlation
tipoDoc is highly overall correlated with CUITHigh correlation
tipoDoc is highly imbalanced (97.1%) Imbalance
nacionalidad is highly imbalanced (94.9%) Imbalance
nacionalidad has 44 (1.2%) missing values Missing
emailAlternativo has 3647 (100.0%) missing values Missing
nroDoc is highly skewed (γ1 = 23.31455586) Skewed
CUIT has unique values Unique
emailAlternativo is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-03-24 22:35:27.785601
Analysis finished2025-03-24 22:37:14.159568
Duration1 minute and 46.37 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

CUIT
Real number (ℝ)

High correlation  Unique 

Distinct3647
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2205399 × 1010
Minimum2.0017346 × 1010
Maximum2.795928 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-03-24T19:37:14.303506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.0017346 × 1010
5-th percentile2.0083137 × 1010
Q12.0182 × 1010
median2.0287289 × 1010
Q32.3379382 × 1010
95-th percentile2.7309133 × 1010
Maximum2.795928 × 1010
Range7.941934 × 109
Interquartile range (IQR)3.1973826 × 109

Descriptive statistics

Standard deviation2.9741399 × 109
Coefficient of variation (CV)0.13393769
Kurtosis-0.86613529
Mean2.2205399 × 1010
Median Absolute Deviation (MAD)1.5188549 × 108
Skewness0.98362253
Sum8.0983091 × 1013
Variance8.8455083 × 1018
MonotonicityNot monotonic
2025-03-24T19:37:14.633637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27331126530 1
 
< 0.1%
27236909900 1
 
< 0.1%
20305924076 1
 
< 0.1%
20082883240 1
 
< 0.1%
20230506060 1
 
< 0.1%
20141208269 1
 
< 0.1%
20271472901 1
 
< 0.1%
20302787221 1
 
< 0.1%
20103522324 1
 
< 0.1%
20264053367 1
 
< 0.1%
Other values (3637) 3637
99.7%
ValueCountFrequency (%)
20017345738 1
< 0.1%
20041217643 1
< 0.1%
20041398125 1
< 0.1%
20041722720 1
< 0.1%
20041901412 1
< 0.1%
20041956799 1
< 0.1%
20042053229 1
< 0.1%
20042977226 1
< 0.1%
20042986624 1
< 0.1%
20043172787 1
< 0.1%
ValueCountFrequency (%)
27959279778 1
< 0.1%
27954188715 1
< 0.1%
27953768521 1
< 0.1%
27949584165 1
< 0.1%
27949461659 1
< 0.1%
27947115893 1
< 0.1%
27946720556 1
< 0.1%
27946027389 1
< 0.1%
27941038951 1
< 0.1%
27940657089 1
< 0.1%

Nombre
Text

Distinct3015
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Memory size255.0 KiB
2025-03-24T19:37:16.103100image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length51
Median length30
Mean length12.347409
Min length3

Characters and Unicode

Total characters45031
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2684 ?
Unique (%)73.6%

Sample

1st rowedit mabel
2nd rowEZEQUIEL MARTÍN
3rd rowHORACIO MIGUEL
4th rowAlfredo
5th rowGABRIEL
ValueCountFrequency (%)
carlos 156
 
2.3%
daniel 148
 
2.2%
maria 146
 
2.1%
juan 140
 
2.1%
luis 139
 
2.0%
alberto 134
 
2.0%
jose 128
 
1.9%
alejandro 120
 
1.8%
jorge 105
 
1.5%
eduardo 103
 
1.5%
Other values (853) 5483
80.6%
2025-03-24T19:37:18.034601image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 3664
 
8.1%
3155
 
7.0%
a 3086
 
6.9%
R 2131
 
4.7%
E 2121
 
4.7%
O 2010
 
4.5%
I 1867
 
4.1%
i 1821
 
4.0%
r 1783
 
4.0%
e 1760
 
3.9%
Other values (55) 21633
48.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45031
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 3664
 
8.1%
3155
 
7.0%
a 3086
 
6.9%
R 2131
 
4.7%
E 2121
 
4.7%
O 2010
 
4.5%
I 1867
 
4.1%
i 1821
 
4.0%
r 1783
 
4.0%
e 1760
 
3.9%
Other values (55) 21633
48.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45031
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 3664
 
8.1%
3155
 
7.0%
a 3086
 
6.9%
R 2131
 
4.7%
E 2121
 
4.7%
O 2010
 
4.5%
I 1867
 
4.1%
i 1821
 
4.0%
r 1783
 
4.0%
e 1760
 
3.9%
Other values (55) 21633
48.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45031
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 3664
 
8.1%
3155
 
7.0%
a 3086
 
6.9%
R 2131
 
4.7%
E 2121
 
4.7%
O 2010
 
4.5%
I 1867
 
4.1%
i 1821
 
4.0%
r 1783
 
4.0%
e 1760
 
3.9%
Other values (55) 21633
48.0%

Apellido
Text

Distinct3049
Distinct (%)83.6%
Missing1
Missing (%)< 0.1%
Memory size235.1 KiB
2025-03-24T19:37:19.272346image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length51
Median length26
Mean length7.4561163
Min length2

Characters and Unicode

Total characters27185
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2760 ?
Unique (%)75.7%

Sample

1st rowRETAMAR
2nd rowDAWIDOWSKI
3rd rowESPOSITO
4th rowSampedro
5th rowALSO
ValueCountFrequency (%)
de 55
 
1.3%
gonzalez 45
 
1.1%
fernandez 36
 
0.9%
garcia 33
 
0.8%
rodriguez 31
 
0.8%
gomez 29
 
0.7%
lopez 23
 
0.6%
diaz 21
 
0.5%
martinez 21
 
0.5%
perez 19
 
0.5%
Other values (2805) 3765
92.3%
2025-03-24T19:37:20.574487image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1976
 
7.3%
a 1690
 
6.2%
E 1320
 
4.9%
R 1318
 
4.8%
e 1235
 
4.5%
O 1164
 
4.3%
I 1123
 
4.1%
o 1120
 
4.1%
r 1103
 
4.1%
i 1102
 
4.1%
Other values (57) 14034
51.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27185
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1976
 
7.3%
a 1690
 
6.2%
E 1320
 
4.9%
R 1318
 
4.8%
e 1235
 
4.5%
O 1164
 
4.3%
I 1123
 
4.1%
o 1120
 
4.1%
r 1103
 
4.1%
i 1102
 
4.1%
Other values (57) 14034
51.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27185
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1976
 
7.3%
a 1690
 
6.2%
E 1320
 
4.9%
R 1318
 
4.8%
e 1235
 
4.5%
O 1164
 
4.3%
I 1123
 
4.1%
o 1120
 
4.1%
r 1103
 
4.1%
i 1102
 
4.1%
Other values (57) 14034
51.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27185
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1976
 
7.3%
a 1690
 
6.2%
E 1320
 
4.9%
R 1318
 
4.8%
e 1235
 
4.5%
O 1164
 
4.3%
I 1123
 
4.1%
o 1120
 
4.1%
r 1103
 
4.1%
i 1102
 
4.1%
Other values (57) 14034
51.6%

tipoDoc
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size213.8 KiB
DNI
3624 
LC
 
9
LE
 
7
CI
 
5
Pasaporte
 
2

Length

Max length9
Median length3
Mean length2.9975322
Min length2

Characters and Unicode

Total characters10932
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDNI
2nd rowDNI
3rd rowDNI
4th rowDNI
5th rowDNI

Common Values

ValueCountFrequency (%)
DNI 3624
99.4%
LC 9
 
0.2%
LE 7
 
0.2%
CI 5
 
0.1%
Pasaporte 2
 
0.1%

Length

2025-03-24T19:37:21.028930image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-24T19:37:21.286920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
dni 3624
99.4%
lc 9
 
0.2%
le 7
 
0.2%
ci 5
 
0.1%
pasaporte 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
I 3629
33.2%
D 3624
33.2%
N 3624
33.2%
L 16
 
0.1%
C 14
 
0.1%
E 7
 
0.1%
a 4
 
< 0.1%
P 2
 
< 0.1%
s 2
 
< 0.1%
p 2
 
< 0.1%
Other values (4) 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10932
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 3629
33.2%
D 3624
33.2%
N 3624
33.2%
L 16
 
0.1%
C 14
 
0.1%
E 7
 
0.1%
a 4
 
< 0.1%
P 2
 
< 0.1%
s 2
 
< 0.1%
p 2
 
< 0.1%
Other values (4) 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10932
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 3629
33.2%
D 3624
33.2%
N 3624
33.2%
L 16
 
0.1%
C 14
 
0.1%
E 7
 
0.1%
a 4
 
< 0.1%
P 2
 
< 0.1%
s 2
 
< 0.1%
p 2
 
< 0.1%
Other values (4) 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10932
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 3629
33.2%
D 3624
33.2%
N 3624
33.2%
L 16
 
0.1%
C 14
 
0.1%
E 7
 
0.1%
a 4
 
< 0.1%
P 2
 
< 0.1%
s 2
 
< 0.1%
p 2
 
< 0.1%
Other values (4) 8
 
0.1%

nroDoc
Real number (ℝ)

High correlation  Skewed 

Distinct3646
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72270510
Minimum1080
Maximum2.7313075 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.2 KiB
2025-03-24T19:37:21.523921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1080
5-th percentile6745894.6
Q114567634
median23335997
Q329322806
95-th percentile39720863
Maximum2.7313075 × 1010
Range2.7313074 × 1010
Interquartile range (IQR)14755173

Descriptive statistics

Standard deviation1.0493586 × 109
Coefficient of variation (CV)14.519873
Kurtosis551.53392
Mean72270510
Median Absolute Deviation (MAD)6881368
Skewness23.314556
Sum2.6357055 × 1011
Variance1.1011535 × 1018
MonotonicityNot monotonic
2025-03-24T19:37:21.842496image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31248337 2
 
0.1%
18507057 1
 
< 0.1%
33112653 1
 
< 0.1%
23690990 1
 
< 0.1%
30592407 1
 
< 0.1%
8288324 1
 
< 0.1%
23050606 1
 
< 0.1%
14120826 1
 
< 0.1%
27147290 1
 
< 0.1%
30278722 1
 
< 0.1%
Other values (3636) 3636
99.7%
ValueCountFrequency (%)
1080 1
< 0.1%
774444 1
< 0.1%
1734573 1
< 0.1%
1791154 1
< 0.1%
2196200 1
< 0.1%
2654011 1
< 0.1%
2935023 1
< 0.1%
3142258 1
< 0.1%
3427647 1
< 0.1%
3490344 1
< 0.1%
ValueCountFrequency (%)
27313074795 1
< 0.1%
27277864458 1
< 0.1%
27269490603 1
< 0.1%
23173177844 1
< 0.1%
20936640223 1
< 0.1%
20309591039 1
< 0.1%
20068691711 1
< 0.1%
2024908763 1
< 0.1%
526312777 1
< 0.1%
394282931 1
< 0.1%

nacionalidad
Categorical

High correlation  Imbalance  Missing 

Distinct24
Distinct (%)0.7%
Missing44
Missing (%)1.2%
Memory size235.0 KiB
Argentina
3527 
Italia
 
9
Paraguay
 
8
España
 
7
Bolivia
 
7
Other values (19)
 
45

Length

Max length9
Median length9
Mean length8.9483764
Min length4

Characters and Unicode

Total characters32241
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.2%

Sample

1st rowArgentina
2nd rowArgentina
3rd rowArgentina
4th rowArgentina
5th rowArgentina

Common Values

ValueCountFrequency (%)
Argentina 3527
96.7%
Italia 9
 
0.2%
Paraguay 8
 
0.2%
España 7
 
0.2%
Bolivia 7
 
0.2%
Perú 6
 
0.2%
Brasil 6
 
0.2%
Uruguay 5
 
0.1%
Venezuela 4
 
0.1%
Chile 4
 
0.1%
Other values (14) 20
 
0.5%
(Missing) 44
 
1.2%

Length

2025-03-24T19:37:22.177457image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
argentina 3527
97.9%
italia 9
 
0.2%
paraguay 8
 
0.2%
españa 7
 
0.2%
bolivia 7
 
0.2%
perú 6
 
0.2%
brasil 6
 
0.2%
uruguay 5
 
0.1%
venezuela 4
 
0.1%
chile 4
 
0.1%
Other values (14) 20
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n 7068
21.9%
a 3628
11.3%
i 3576
11.1%
r 3564
11.1%
e 3555
11.0%
g 3543
11.0%
t 3539
11.0%
A 3533
11.0%
l 37
 
0.1%
u 25
 
0.1%
Other values (26) 173
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 7068
21.9%
a 3628
11.3%
i 3576
11.1%
r 3564
11.1%
e 3555
11.0%
g 3543
11.0%
t 3539
11.0%
A 3533
11.0%
l 37
 
0.1%
u 25
 
0.1%
Other values (26) 173
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 7068
21.9%
a 3628
11.3%
i 3576
11.1%
r 3564
11.1%
e 3555
11.0%
g 3543
11.0%
t 3539
11.0%
A 3533
11.0%
l 37
 
0.1%
u 25
 
0.1%
Other values (26) 173
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 7068
21.9%
a 3628
11.3%
i 3576
11.1%
r 3564
11.1%
e 3555
11.0%
g 3543
11.0%
t 3539
11.0%
A 3533
11.0%
l 37
 
0.1%
u 25
 
0.1%
Other values (26) 173
 
0.5%

email
Text

Distinct3522
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size291.1 KiB
2025-03-24T19:37:23.168439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length44
Median length39
Mean length24.691801
Min length10

Characters and Unicode

Total characters90051
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3487 ?
Unique (%)95.6%

Sample

1st rowedit-retamar@hotmail.com
2nd rowlazarodawi@hotmail.com
3rd rowesposito.h@gmail.com
4th rowservicentrorefrigeracion@yahoo.com.ar
5th rowgabrielalso@outlook.com
ValueCountFrequency (%)
artisticacck@gmail.com 78
 
2.1%
artisticagrl@gmail.com 7
 
0.2%
leonardorilo@starnovagroup.com 7
 
0.2%
libreria_centro@hotmail.com 3
 
0.1%
miguel_lamera@hotmail.com 3
 
0.1%
grupolmmendoza@gmail.com 3
 
0.1%
rodriguezs@creditoautomatico.com.ar 3
 
0.1%
brocazfernando@gmail.com 2
 
0.1%
marceseig@hotmail.com 2
 
0.1%
jcasociados@hotmail.com 2
 
0.1%
Other values (3510) 3537
97.0%
2025-03-24T19:37:24.337694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 9964
 
11.1%
o 9117
 
10.1%
m 7530
 
8.4%
i 6976
 
7.7%
c 6041
 
6.7%
l 5126
 
5.7%
. 4930
 
5.5%
r 4863
 
5.4%
e 4486
 
5.0%
@ 3647
 
4.0%
Other values (56) 27371
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90051
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 9964
 
11.1%
o 9117
 
10.1%
m 7530
 
8.4%
i 6976
 
7.7%
c 6041
 
6.7%
l 5126
 
5.7%
. 4930
 
5.5%
r 4863
 
5.4%
e 4486
 
5.0%
@ 3647
 
4.0%
Other values (56) 27371
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90051
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 9964
 
11.1%
o 9117
 
10.1%
m 7530
 
8.4%
i 6976
 
7.7%
c 6041
 
6.7%
l 5126
 
5.7%
. 4930
 
5.5%
r 4863
 
5.4%
e 4486
 
5.0%
@ 3647
 
4.0%
Other values (56) 27371
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90051
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 9964
 
11.1%
o 9117
 
10.1%
m 7530
 
8.4%
i 6976
 
7.7%
c 6041
 
6.7%
l 5126
 
5.7%
. 4930
 
5.5%
r 4863
 
5.4%
e 4486
 
5.0%
@ 3647
 
4.0%
Other values (56) 27371
30.4%

emailAlternativo
Unsupported

Missing  Rejected  Unsupported 

Missing3647
Missing (%)100.0%
Memory size28.6 KiB

estadoCivil
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size226.6 KiB
Soltero
2141 
Casado
1506 

Length

Max length7
Median length7
Mean length6.5870579
Min length6

Characters and Unicode

Total characters24023
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCasado
2nd rowSoltero
3rd rowCasado
4th rowSoltero
5th rowSoltero

Common Values

ValueCountFrequency (%)
Soltero 2141
58.7%
Casado 1506
41.3%

Length

2025-03-24T19:37:24.692856image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-24T19:37:24.991411image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
soltero 2141
58.7%
casado 1506
41.3%

Most occurring characters

ValueCountFrequency (%)
o 5788
24.1%
a 3012
12.5%
S 2141
 
8.9%
l 2141
 
8.9%
e 2141
 
8.9%
t 2141
 
8.9%
r 2141
 
8.9%
C 1506
 
6.3%
s 1506
 
6.3%
d 1506
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24023
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5788
24.1%
a 3012
12.5%
S 2141
 
8.9%
l 2141
 
8.9%
e 2141
 
8.9%
t 2141
 
8.9%
r 2141
 
8.9%
C 1506
 
6.3%
s 1506
 
6.3%
d 1506
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24023
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5788
24.1%
a 3012
12.5%
S 2141
 
8.9%
l 2141
 
8.9%
e 2141
 
8.9%
t 2141
 
8.9%
r 2141
 
8.9%
C 1506
 
6.3%
s 1506
 
6.3%
d 1506
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24023
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5788
24.1%
a 3012
12.5%
S 2141
 
8.9%
l 2141
 
8.9%
e 2141
 
8.9%
t 2141
 
8.9%
r 2141
 
8.9%
C 1506
 
6.3%
s 1506
 
6.3%
d 1506
 
6.3%

Interactions

2025-03-24T19:36:11.959111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-24T19:35:29.602014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-24T19:36:34.145286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-03-24T19:35:42.180214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-03-24T19:37:25.233004image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
CUITestadoCivilnacionalidadnroDoctipoDoc
CUIT1.0001.0001.0000.5171.000
estadoCivil1.0001.0000.0000.0170.000
nacionalidad1.0000.0001.0000.0800.000
nroDoc0.5170.0170.0801.0000.033
tipoDoc1.0000.0000.0000.0331.000

Missing values

2025-03-24T19:37:13.473029image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-24T19:37:13.826780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-24T19:37:14.070255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreApellidotipoDocnroDocnacionalidademailemailAlternativoestadoCivil
027236909900edit mabelRETAMARDNI23690990Argentinaedit-retamar@hotmail.comNaNCasado
120305924076EZEQUIEL MARTÍNDAWIDOWSKIDNI30592407Argentinalazarodawi@hotmail.comNaNSoltero
220082883240HORACIO MIGUELESPOSITODNI8288324Argentinaesposito.h@gmail.comNaNCasado
320230506060AlfredoSampedroDNI23050606Argentinaservicentrorefrigeracion@yahoo.com.arNaNSoltero
420141208269GABRIELALSODNI14120826Argentinagabrielalso@outlook.comNaNSoltero
520271472901luciano hernandizDNI27147290Argentinalucianohdiz@gmail.comNaNSoltero
620302787221Martin HernanBarbatelliDNI30278722Argentinamartin.barbatelli@gmail.comNaNSoltero
720103522324Dante OscarRiverosDNI10352232Argentinaagrimdanteriveros@gmail.comNaNCasado
820264053367LEANDROBOBADILLADNI26405336Argentinaservisurmdp@gmail.comNaNSoltero
920310857751Pablo Martinde la CruzDNI31085775Argentinainfo@pablodelacruzeventos.comNaNSoltero
CUITNombreApellidotipoDocnroDocnacionalidademailemailAlternativoestadoCivil
363720385356243JONATAN MACIELDIAZDNI38535624Argentinamacijon10@gmail.comNaNSoltero
363820322310944Ezequiel FernandoRodríguezDNI32231094Argentinaefrrodriguez1724@gmail.comNaNSoltero
363920149549987LUIS MARIAROBOLDNI14954998Argentinabrunorobol@transporterobol.comNaNCasado
364024925253304FREDY MARTINALBERTIDNI92525330Uruguaytincho75@yahoo.comNaNSoltero
364120328156742José MaríaAlegreDNI32815674Argentinamonachitapapandrew@gmail.comNaNSoltero
364220171591563Julio GustavoLazzosDNI17159156Argentinabiotecnika@hotmail.comNaNSoltero
364320293290416ELIAS MARTINSEGURADNI29329041Argentinae.segura1982@gmail.comNaNSoltero
364420240423759FEDERICO MARTINNUÑEZDNI24042375Argentinafederico@inspira.arNaNCasado
364520287286687MARIO CEFERINOLAZARTEDNI28728668Argentinalazarte.events@gmail.comNaNSoltero
364627331126530Soledad AnahiCerillanoDNI33112653Argentinasolecerillano@gmail.comNaNSoltero